The ANN with back propagation algorithm is a multi-layer feed-forward neural network, which is suitable to study unsteady frost formation with multiple factors. The backpropagation ANN algorithm is used to study fros...
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The ANN with back propagation algorithm is a multi-layer feed-forward neural network, which is suitable to study unsteady frost formation with multiple factors. The backpropagation ANN algorithm is used to study frost layer growth on cold flat surface, where four feature variables including temperature of cold flat sur-face, the velocity, relative humidity, and temperature of air are adopted. The frost growth experiment generates the database, which is good for training frost growth due to its fast speed and high precision based on Levenberg-Marquardt learning rule. The establishment of neural network model in this paper can quickly and accurately predict the frost layer height on cold flat surface of different control variables, which is helpful for the implementation of defrosting.
X-ray diffractometry is a unique technique and that the X-ray diffraction patterns which depict the structure of the steel sheets during processing with the features extracted, that they serve directly as a signature ...
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X-ray diffractometry is a unique technique and that the X-ray diffraction patterns which depict the structure of the steel sheets during processing with the features extracted, that they serve directly as a signature which is very complicated. X-ray diffraction (XRD) techniques are a type of non-destructive method of investigation to identify the flaws during the fabrication of steel sheets. X-ray diffraction is comparatively simple and can be effectively used for the examination and identification of flaws during the rolling process of steel sheets. XRD technique finds application in various fields like textile industry, forensic, qualitative and quantitative phase analysis of poly crystalline material, to infer overall properties of the fiber and measure the degree of crystalline nature. It is extensively used to explore areas like material science, chemistry and in industry for research and quality control. This effective method gains novelty by combining the signal processing algorithms like multiple threshold based Fast Fourier Transform (FFT) and Artificial neural network (ANN) trained with back propagation algorithm (BPA) thereby offering an automated system for online monitoring during fabrication of flawless metal sheets. The hot rolled steel sheets for three categories namely, flawless, moderate flaw and extreme flaw conditions are obtained from the XRD pattern. Then multiple thresholds are incorporated to identify the peak position, peak width and peak intensity. The FFT algorithm computes the power spectrum which is used as features to identify the flaws in the steel sheets during cold rolling process. The extracted features are used as inputs to train the ANN with BPA whose performance is evaluated to be 90% efficient. (C) 2020 The Authors. Published by Elsevier Ltd.
Brushless doubly fed induction generator (BDFIG) has great potential due to its high reliability and low maintenance cost. To achieve high-performance modeling and control, BDFIG resistances and inductances are necess...
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Brushless doubly fed induction generator (BDFIG) has great potential due to its high reliability and low maintenance cost. To achieve high-performance modeling and control, BDFIG resistances and inductances are necessary. However, the existing identification methods either required professional structure knowledge or special excitations and setups, or only estimated part of parameters. Thus, this letter proposes a multilayer full-parameter identification model based on the back-propagation (BP) algorithm for BDFIG, which is constructed with electric quantities as nodes and parameters as adjustable weights, and utilizes the electric quantities measured from regular operations as data. According to the fitting error obtained by comparing the model outputs with the easily measured references, the BP algorithm is applied to update the weights until the error is sufficiently small. Then, all resistances and inductances can be extracted directly from the weights. Such an identification methodology can be easily embedded into existing BDFIG systems. The simulations and experiments verify its feasibility and accuracy.
In order to solve the problems of large energy consumption in many stations, confusion lines and lighting source not accord with environmental protection requirement in the present, we put forward the scheme of automa...
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In order to solve the problems of large energy consumption in many stations, confusion lines and lighting source not accord with environmental protection requirement in the present, we put forward the scheme of automatic control system of green lighting based on PROFIBUS and PLC. In order to realise the inherent limitation of lighting control system in information sharing, remote control and other aspects, web services technology is combined with the original system to make full use of its technical advantages in distribution, openness and flexibility, so as to explore a better way for the development of industrial lighting network application. By constructing the communication network of the station lighting system with the PROFIBUS communication protocol and designing the hardware circuit and software program of the system bottom with programmable controller technology;simultaneously, the proposed BP neural network algorithm in the control system can realise intelligent control. Simulating with MATLAB of green computing for multimedia big data, the result shows that the station green lighting control system has very important significance of energy saving and provide the passenger for healthy, comfortable environment.
X-ray diffractometry is a unique technique and that the X-ray diffraction patterns which depict the structure of the steel sheets during processing with the features extracted, that they serve directly as a signature ...
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X-ray diffractometry is a unique technique and that the X-ray diffraction patterns which depict the structure of the steel sheets during processing with the features extracted, that they serve directly as a signature which is very complicated. X-ray diffraction (XRD) techniques are a type of non-destructive method of investigation to identify the flaws during the fabrication of steel sheets. X-ray diffraction is comparatively simple and can be effectively used for the examination and identification of flaws during the rolling process of steel sheets. XRD technique finds application in various fields like textile industry, forensic, qualitative and quantitative phase analysis of poly crystalline material, to infer overall properties of the fiber and measure the degree of crystalline nature. It is extensively used to explore areas like material science, chemistry and in industry for research and quality control. This effective method gains novelty by combining the signal processing algorithms like multiple threshold based Fast Fourier Transform (FFT) and Artificial neural network (ANN) trained with back propagation algorithm (BPA) thereby offering an automated system for online monitoring during fabrication of flawless metal sheets. The hot rolled steel sheets for three categories namely, flawless, moderate flaw and extreme flaw conditions are obtained from the XRD pattern. Then multiple thresholds are incorporated to identify the peak position, peak width and peak intensity. The FFT algorithm computes the power spectrum which is used as features to identify the flaws in the steel sheets during cold rolling process. The extracted features are used as inputs to train the ANN with BPA whose performance is evaluated to be 90% efficient.
Error correction codes such as low density parity check (LDPC) codes are popularly used to enhance the performance of digital communication systems. The current decoding framework relies on exchanging beliefs over a T...
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ISBN:
(纸本)9781728119854
Error correction codes such as low density parity check (LDPC) codes are popularly used to enhance the performance of digital communication systems. The current decoding framework relies on exchanging beliefs over a Tanner graph, which the encoder and decoder are aware of. However, this information may not be available readily, for example in covert communication. The main idea of this paper is to build a neural network to learn the encoder mappings in the absence of knowledge of the Tanner graph. We propose a scheme to learn the mappings using the back propagation algorithm. We investigate into the choice of different cost functions and the number of hidden neurons for learning the encoding function. The proposed scheme is capable of learning the parity check equations over a binary field towards identifying the validity of a codeword. Simulation results over synthetic data show that our algorithm is indeed capable of learning the encoder mappings and identifying the parity check equations.
This paper presents the forecasting of wind energy using neural networks based on the speed and the rotational speed of the rotor. The wind power mainly depends on the velocity of the wind, density, and swept area of ...
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ISBN:
(纸本)9781538693469
This paper presents the forecasting of wind energy using neural networks based on the speed and the rotational speed of the rotor. The wind power mainly depends on the velocity of the wind, density, and swept area of the wind mill which depends on the radius of the rotor. The power also depends on the efficiency of the motor, gear mechanisms. Hence the power varies according to the various parameters which makes the model non-linear. In this paper neural network is used to predict the output from the previous set of data. A new set of data is chosen and tested for the accuracy. It can be seen that the back propagation algorithm in neural network is able to classify the power output based on the speed of the rotor and the wind velocity.
In this paper, the backpropagation (BP) neural network wind power prediction model and the BP neural network wind power model with numerical weather prediction are established. The research shows that the BP neural n...
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ISBN:
(纸本)9781450376631
In this paper, the backpropagation (BP) neural network wind power prediction model and the BP neural network wind power model with numerical weather prediction are established. The research shows that the BP neural network model with numerical weather prediction can improve the prediction accuracy more effectively.
This work presents the prediction of plasmon positions in the optical absorption spectra of silver nanorods using artificial neural networks. Silver nanorods were prepared using a modified seed mediated strategy. At f...
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This work presents the prediction of plasmon positions in the optical absorption spectra of silver nanorods using artificial neural networks. Silver nanorods were prepared using a modified seed mediated strategy. At first, seed particles were synthesized by reducing silver ions with sodium borohydride in the presence of a stabilizer viz., cetyltrimethylammonium bromide (CTAB). In order to produce silver nanorods, the seeds were added to a growth solution containing a metal salt (AgNO3), a weak reducing agent (ascorbic acid) and a structure directing agent (CTAB). Four parameters viz., volume of metal seed, concentrations of silver nitrate, ascorbic acid and CTAB, which are found to be highly sensitive to plasmon characteristics are varied and grouped into 45 different sets of experiments. Herein, we report a model to predict the transverse and longitudinal plasmon band wavelengths, using a 4-input artificial neural network, having five neurons in the hidden layer and two neurons in the output layer. Levenberg Marquardt back-propagationalgorithm was used for the design. The network architecture has been trained with the 45 sets of experimental data collected and the regression plots with good correlation coefficient values were obtained for training, testing and validation stages.
In the past years modern arithmetical methods for image investigation have led to a rebellion in many fields, from computer vision to scientific imaging. Though, some recently developed image processing techniques suc...
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ISBN:
(纸本)9781509012855
In the past years modern arithmetical methods for image investigation have led to a rebellion in many fields, from computer vision to scientific imaging. Though, some recently developed image processing techniques successfully oppressed by other sections have been infrequently, if ever, experimented on celestial observations. Here we present a new idea of super resolution of Astronomical objects using back propagation algorithm."Super-resolution " is efficient in improving the excellence of analysis of diffused sources formerly unobserved by the background noise, efficiently rising the depth of obtainable observations. Higher-resolution image out of a set of low resolution frames can be obtained through super-resolution. Super-resolution is viable only for point sources which have negligible dimensions, then for wide-ranging objects the knowledge about intensity vacillation at angular prevalence is irreversibly mislaid. Again obtaining super resolved image for extended sources(e.g. comets, meteoroids, etc) is a new challenge if the speed of the object is very high. Acquiring High resolution images of celestial objects from ground based telescopes is intricate and often requires computational post processing techniques to remove blur caused by atmospheric commotion. Even images obtained through satellite imaging are compressed and sent to earth. So there is need for Super resolution of those compressed or noisy images. So, here we simply implement Super-resolution for Astronomical objects using back propagation algorithm to overcome lost information and challenges for high speedy celestial objects. The purpose is to super resolve high speedy celestial objects whose analysis may in future help to prevent collisions of such celestial objects with earth and also avoid future solar system damage
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